Differences in distribution and features of carotid and middle cerebral artery plaque in patients with pial infarction and perforating artery infarction: A 3D vessel wall imaging study.


Journal

European journal of radiology
ISSN: 1872-7727
Titre abrégé: Eur J Radiol
Pays: Ireland
ID NLM: 8106411

Informations de publication

Date de publication:
Oct 2023
Historique:
received: 20 03 2023
revised: 31 05 2023
accepted: 12 08 2023
medline: 25 9 2023
pubmed: 17 8 2023
entrez: 16 8 2023
Statut: ppublish

Résumé

Atherosclerotic plaques of carotid artery (CA) and middle cerebral artery (MCA) are important causes of acute ischemic stroke (AIS). This study was designed to jointly assess the plaque distribution and features of CA and MCA in AIS patients with pial infarction (PI) and perforating artery infarction (PAI), and to investigate the associations between plaque characteristics and ischemic infarction patterns. Imaging data of sixty-five patients from a cross-sectional study were reviewed. All the patients had acute infarction in the MCA territory on diffusion weighted imaging (DWI) and underwent CA and MCA vessel wall imaging (VWI). The CA and MCA plaque presence and high-risk features on the ipsilateral side of infarction were analyzed. The brain infarction lesions were divided into PI group vs. non-PI group, and PAI group vs. non-PAI group. Different plaque distribution types and plaque features were compared in each two groups, and their associations were investigated using binary logistic regression. Sixty-five patients (mean age, 54.6 ± 10.1 years; 61 men) were included. The CA high-risk plaque (OR: 5.683 [1.409-22.929], P = 0.015) and MCA plaque presence (OR: 3.949 [1.397-11.162], P = 0.010) were significantly associated with PI. MCA plaques that involved the orifice of the perforating arteries were significantly associated with PAI (OR: 15.167 [1.851-124.257], P = 0.011). CA and MCA plaques show distinct distribution and high-risk features in patients with PI and PAI. Combined intracranial and extracranial arteries imaging should be considered for the evaluation of the symptomatic ischemic patients.

Identifiants

pubmed: 37586303
pii: S0720-048X(23)00359-5
doi: 10.1016/j.ejrad.2023.111045
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

111045

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Mi Shen (M)

Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Peiyi Gao (P)

Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Neurosurgical Institute, Beijing, China. Electronic address: cjr.gaopeiyi@vip.163.com.

Shuo Chen (S)

Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.

Xihai Zhao (X)

Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.

Rui Li (R)

Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.

Wanliang Du (W)

Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Chun Yuan (C)

Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China; Department of Radiology, University of Washington, Seattle, WA, USA.

Thomas Hatsukami (T)

Department of Surgery, University of Washington, Seattle, WA, USA.

Binbin Sui (B)

Beijing Neurosurgical Institute, Beijing, China; Tiantan Neuroimaging Center for Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China. Electronic address: reneesui@163.com.

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Classifications MeSH